A risk signature with four autophagy‐related genes for predicting survival of glioblastoma multiforme

Wang, Yulin; Zhao, Wenjin; Xiao, Zhe; Guan, Gefei; Liu, Xin; Zhuang, Minghua

doi:10.1111/jcmm.14938

Cited by 59 publications

(73 citation statements)

References 57 publications

(57 reference statements)

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“…These ndings suggested our new risk score may be a promising biomarker for GBM diagnosis and prognosis. In order to obtain better predictive effects in clinic, recent studies recommended to creating a nomogram that integrated the molecular signature with clinical indicators [7,10,17,18]. Similarly, a nomogram based on the risk signature, age, recurrence status and IDH mutation status was established in the training cohort.…”

Section: Discussionmentioning

confidence: 99%

Identification of an Autophagy-Related 10-lncRNA-mRNA Signature for Distinguishing Glioblastoma Multiforme from Lower‑Grade Glioma and Prognosis Prediction

Wei

Wang

Zhao

2020

Preprint

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Background: Autophagy provides the nutrients and energy for tumor growth, invasion and metastasis. Theoretically, autophagy-related mRNAs and regulatory long non-coding RNAs (lncRNAs) may represent promising biomarkers to predict tumor progression and poor prognosis. Our study aims to develop an autophagy-related signature to distinguish glioblastoma (GBM) from lower-grade gliomas (LGG) and predict overall survival (OS).Methods: The expression profile of GBM and LGG was collected from the Chinese Glioma Genome Atlas (CGGA) database that was used to identify differentially expressed genes (DEGs) and lncRNAs (DELs). The autophagy-related genes were obtained from the Human Autophagy Database. The autophagy-related DELs were identified by a co-expression network with DEGs. These DEGs and DELs underwent univariate and multivariate analyses to screen prognostic genes. They were entered into the Logit regression model to identify the GBM feature genes. The prognostic signature was evaluated by survival curve analyses and validated using The Cancer Genome Atlas (TCGA) dataset. The prognostic model and clinicopathological parameters were integrated to construct the nomogram.Results: A total of 131 autophagy-related DEGs and 54 autophagy-related DELs were identified. Ten of them were demonstrated as independent prognostic factors and could distinguish GBM from LGG, with the accuracy of 0.891 using CGGA dataset and 0.790 using TCGA dataset. The risk score was established based on these 10 genes. Patients with higher risk score were at an increased risk of developing GBM (49.7% vs 21.3%) and worse OS prognosis than those in low risk group. The predictive accuracy was 0.840 and 0.744 for CGGA and TCGA dataset, respectively. Multivariate analysis showed age, recurrence, IDH mutation and risk score status were independent prognostic factors and thus they were used to build a nomogram which showed the highest predictive power than other factors.Conclusion: The established nomogram may aid the clinical decision making of personalized treatment.

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Section: Discussionmentioning

confidence: 99%

Identification of an Autophagy-Related 10-lncRNA-mRNA Signature for Distinguishing Glioblastoma Multiforme from Lower‑Grade Glioma and Prognosis Prediction

Wei

Wang

Zhao

2020

Preprint

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“…The Human Autophagy Database (http://www.autophagy.lu/index.html) was used to download the entire 232 ARGs [14]. RNA sequencing data and the corresponding clinical data for 374 HCC and 50 non-tumor tissues were acquired from the TCGA database (https://tcga-data.nci.nih.gov/tcga/) [15].…”

Section: Data Collection and Processionmentioning

confidence: 99%

“…Univariate Cox regression and multivariate Cox regression analyses were applied to pick out the ARGs that were closely related to OS in HCC [18]. We also performed the Lasso regression to remove ARGs that might be closely correlated with others [14,16].…”

Section: Differentially Expressed Args In Hccmentioning

confidence: 99%

“…A linear combination of the expression of ARGs and regression coe cient which based on the multivariate Cox regression was used to construct the risk signature [14]. The OS-related predictive formula was calculated as following: risk score = (Expression gene1 × Coe cient gene1 ) + (Expression gene2 × Coe cient gene2 ) +…+ (Expression genen × Coe cient genen ) [14]. Then, each patient with HCC was assigned a risk score according to the formula.…”

Section: Establishment Of Prognostic Model For Hccmentioning

confidence: 99%

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Development and validation of a survival model based on autophagy-associated genes for predicting prognosis of hepatocellular carcinoma

Yang

Niu

Zhao³

et al. 2020

Preprint

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Background: Hepatocellular carcinoma (HCC) is one of the devastating tumors with increasing incidence. Autophagy-associated genes (ARGs) are widely participated in the cellular processes of HCC. This study proposed to identify the novel prognostic gene signature based on ARGs in HCC. Methods: We downloaded the RNA sequencing data and clinical information of HCC and normal tissues from The Cancer Genome Atlas (TCGA) database. The differentially expressed ARGs were screened by the Wilcoxon signed-rank test. Functional enrichment analyses were conducted to explore the biological implications and mechanisms of ARGs in HCC. Cox regression analysis and Lasso regression analysis were performed to screen the ARGs which related to overall survival (OS). The OS-related ARGs were then used to establish a prognostic prediction model. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were both applied to evaluate the accuracy of the model. GSE14520 dataset was downloaded as the testing cohort to validate the prognostic risk model in TCGA. A nomogram based on the clinical features and risk signature was established to predict the 3-year and 5-year survival rate of HCC patients. Results: Totally 27 differentially expressed ARGs were screened in this study. Then, 3 OS-related ARGs (SQSTM1, HSPB8, and BIRC5) were identified via the Cox regression and Lasso regression analyses. Based on these 3 ARGs, a prognostic prediction model was constructed. HCC patients in high-risk group presented poorer prognosis than those with low risk score in TCGA cohort (3-year OS, 53.7% vs 70.2%; 5-year OS, 42.0 % vs 55.2%; P=4.478e-04) and in the testing group (3-year OS, 57.7% vs 73.5%; 5-year OS, 43.2% vs 63.0%; P=1.274e-03). The risk score curve showed a well feasibility in predicting the patients’ survival both in TCGA and GEO cohort with the area under the ROC curve (AUC) of 0.756 and 0.672, respectively. Besides, the calibration curves and C-index indicated that the clinical nomogram performs well to predict the 3-year and 5-year survival rate in HCC patients. Conclusions: The survival model based on the ARGs may be a promising tool to predict the prognosis in HCC patients.

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“…Each patient with ESCA was assigned a risk score according to the formula. The predictive formula was calculated as following: risk score = (Coe cient gene1 × Expression gene1 ) + (Coe cient gene2 × Expression gene2 ) [24]. The Kaplan-Meier curve and receiver operating characteristic (ROC) curve were applied to assess the predictive e ciency of the gene signature.…”

Section: Construction Of Prognostic Gene Signaturementioning

confidence: 99%

Identification and validation of a novel prognostic index based on m6A RNA methylation regulators in esophageal carcinoma

Yang¹,

Duan²,

Zhao³

et al. 2020

Preprint

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Background The incidence and mortality rate of esophageal carcinoma (ESCA) remains high. This study proposed to explore the promising prognostic markers based on m6A RNA methylation regulators, and finally to improve the prognostic assessment for ESCA patients. Methods The RNA sequencing and relevant clinical data of ESCA and normal tissues were obtained from The Cancer Genome Atlas (TCGA) database. Then, we evaluated the expression pattern of 13 m6A methylation regulators in ESCA and normal samples. Two groups of ESCA were divided by the consensus clustering analysis. STRING database and R package were used to construct the protein-protein interaction network and conduct correlation analysis, respectively. Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to develop the multiple-gene risk signature. Kaplan-Meier method and receiver operating characteristic (ROC) curves were used to assess the accuracy of the model. The clinical nomogram combining clinicpathological factors and gene signature was built to predict survival rate of ESCA patients. Gene set enrichment analysis (GSEA) and networks prediction analysis were conducted to explore the signaling pathways that related to the risk genes. Results Eight m6A methylation regulators (HNRNPC, YTHDF1, METTL3, YTHDF2, WTAP, YTHDC1, KIAA1429, and RBM15) were significantly upregulated in ESCA. Two clusters of ESCA with obvious differences in tumor stage were identified via the consensus clustering analysis. A two-gene signature, ALKBH5 and HNRNPC, was established for predicting the prognosis of ESCA patients. Kaplan-Meier curves illustrated that the overall survival of patients in low-risk group was obviously longer than that of patients in high-risk group (P = 1.411e-02). Importantly, risk score and tumor stage were identified as the independent prognostic indicators. The testing dataset GSE13898 showed that the nomogram had a good capacity to assess the prognosis of ESCA patients. Cell cycle, mTOR pathway, and p53 signaling pathway were found to be related to the dysregulation of risk genes. Conclusions m6A RNA methylation regulators ALKBH5 and HNRNPC could act as prognostic indicators and therapeutic targets for prognostic analysis and cancer treatment of ESCA.

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A risk signature with four autophagy‐related genes for predicting survival of glioblastoma multiforme

Cited by 59 publications

References 57 publications

Identification of an Autophagy-Related 10-lncRNA-mRNA Signature for Distinguishing Glioblastoma Multiforme from Lower‑Grade Glioma and Prognosis Prediction

Identification of an Autophagy-Related 10-lncRNA-mRNA Signature for Distinguishing Glioblastoma Multiforme from Lower‑Grade Glioma and Prognosis Prediction

Development and validation of a survival model based on autophagy-associated genes for predicting prognosis of hepatocellular carcinoma

Identification and validation of a novel prognostic index based on m6A RNA methylation regulators in esophageal carcinoma

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